Chubb
From Chubb's careers page
Status: listed on the employer's own careers page when we last checked (daily). Confirm on their site before applying.
Role Summary
Design, build, and maintain scalable data solutions that support reporting, self-service analytics, and advanced analytics.
Work primarily on the organization’s data platform, currently centered on Snowflake and IICS, to transform raw data into trusted, business-ready data assets.
Partner closely with Data Analysts and source-system teams to translate business needs into data models, pipelines, and reusable data products.
Use AI-assisted development practices across the full delivery lifecycle to improve design quality, development efficiency, testing, documentation, and technical communication.
Apply modern data engineering practices while maintaining strong fundamentals in data modeling, ETL/ELT, orchestration, and data quality.
Design, develop, and maintain data pipelines, transformations, and orchestration workflows that ingest, curate, and publish data for analytics use cases.
Build and enhance data models based on business analysis and reporting requirements.
Work with structured and semi-structured data from enterprise systems and files, including relational databases, JSON, and XML.
Collaborate with Data Analysts, source-system developers, and business stakeholders to define requirements and deliver fit-for-purpose data solutions.
Improve engineering productivity by using AI tools for code support, testing framework creation, documentation, diagramming, and solution exploration.
Support data quality, performance optimization, maintainability, and operational reliability of data solutions.
Enable business reporting, self-service analytics, and foundational data assets for advanced analytics and data science teams.
Follow established organizational standards for governance, security, access control, and data handling.
Background in programming, databases, data engineering, analytics engineering, or related technical disciplines; or degree in computer science, software engineering, information systems, economics, or other relevant engineering fields.
Experience working with data warehousing, data marts, reporting environments, and cloud-based data platforms.
Experience building ETL/ELT pipelines and supporting analytics-focused data solutions
Functional Competencies
Knowledge of data and analytics frameworks supporting data lakes, warehouses, marts, and reporting.
Strong understanding of data modeling principles and the ability to design solutions aligned with business objectives.
Experience using AI for data engineering tasks such as design support, testing, documentation, structured prompting, and workflow improvement.
Ability to work across the data lifecycle, from ingestion and transformation through delivery of trusted analytical data sets.
Understanding of data quality, validation, monitoring, retention considerations, and operational support requirements.
Ability to balance modern engineering practices with strong core data development fundamentals.
Strong collaboration and communication skills when working with analysts, developers, and business stakeholders.
Technical Competencies
Strong SQL and Python skills.
Hands-on experience with Snowflake and ETL/ELT or data integration platforms; IICS experience is strongly preferred.
Experience with data orchestration and workflow tools; familiarity with Airflow is a plus.
Experience with data transformation and modeling tools such as dbt or equivalent frameworks.
Familiarity with CI/CD practices, version control, and testing approaches for data solutions.
Experience working with relational databases such as SQL Server and PostgreSQL.
Ability to work with APIs and semi-structured data formats such as JSON and XML.
Understanding of cloud and DevOps concepts is a plus.
Ability to adapt to evolving tools, platforms, and AI-assisted engineering practices.
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